Crespo-Cadenas Carlos, Madero-Ayora María José, Becerra Juan A, Marqués-Valderrama Elías, Cruces Sergio
Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Seville, Spain.
Sensors (Basel). 2025 Jul 9;25(14):4266. doi: 10.3390/s25144266.
Digital predistortion and nonlinear behavioral modeling of power amplifiers (PA) have been the subject of intensive research in the time domain (TD), in contrast with the limited number of works conducted in the frequency domain (FD). However, the adoption of orthogonal frequency division multiplexing (OFDM) as a prevalent modulation scheme in current wireless communication standards provides a promising avenue for employing an FD approach. In this work, a procedure to model nonlinear distortion in wireless OFDM systems in the frequency domain is demonstrated for general model structures based on a sparse Bayesian learning (SBL) algorithm to identify a reduced set of regressors capable of an efficient and accurate prediction. The FD-SBL algorithm is proposed to first identify the active FD regressors and estimate the coefficients of the PA model using a given symbol, and then, the coefficients are employed to predict the distortion of successive OFDM symbols. The performance of this proposed FD-SBL with a validation NMSE of -47 dB for a signal of 30 MHz bandwidth is comparable to -46.6 dB of the previously proposed implementation of the TD-SBL. In terms of execution time, the TD-SBL fails due to excessive processing time and numerical problems for a 100 MHz bandwidth signal, whereas the FD-SBL yields an adequate validation NMSE of -38.6 dB.
与在频域（FD）开展的研究工作数量有限形成对比的是，功率放大器（PA）的数字预失真和非线性行为建模一直是时域（TD）深入研究的主题。然而，正交频分复用（OFDM）作为当前无线通信标准中一种普遍的调制方案，为采用频域方法提供了一条很有前景的途径。在这项工作中，针对基于稀疏贝叶斯学习（SBL）算法的通用模型结构，展示了一种在频域中对无线OFDM系统中的非线性失真进行建模的过程，以识别一组能够进行高效准确预测的简化回归器集。提出了频域 - SBL算法，首先使用给定符号识别有源频域回归器并估计PA模型的系数，然后，利用这些系数预测连续OFDM符号的失真。对于带宽为30 MHz的信号，所提出的频域 - SBL的验证归一化均方误差（NMSE）为 - 47 dB，与先前提出的时域 - SBL实现的 - 46.6 dB相当。在执行时间方面，对于带宽为100 MHz的信号，时域 - SBL由于处理时间过长和数值问题而失败，而频域 - SBL产生了足够的 - 38.6 dB的验证NMSE。
